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Don't go against the cycles; this is the most important lesson I learned from the last cycle. There is no super cycle, and don't blindly trust the big players; several of them get sacrificed each round.
In the past few days, I reviewed the current bear market, which is basically following the same pattern as the last bear market.
Last cycle (May 2022): $30,000 was considered the iron bottom of the super cycle at that time because it was the starting point after the disaster on May 19, 2021, and also the support of the weekly 120. I particularly remember that when it broke below $30,000, the fear index was already below 10, and a lot of bottom-fishing funds believed it couldn't drop further or that it was time to rebound.
Current cycle (February 2026): $80,000, which used to be a strong support level, has now become a strong resistance level at the weekly MA120. $80,000 is also the cost price for Bitcoin mining companies. The current $76,000 feels like the brief struggle after breaking below $30,000 in the last cycle; the market may very well have one more action that thoroughly shatters confidence, such as touching the peak of the last cycle at $69,000.
If it falls below $70,000, it will trigger a larger-scale stop-loss and liquidation, which could potentially exceed the massive trading volume from November 2025. Without extreme panic, rebounds often just serve to entice buyers.
During the cycle, never let emotions lead you astray; in extreme trend markets, emotional indicators can be misleading. A fear index of 10 indicates that retail investors are already hopeless, but the main players may still be using this despair for one last deep squat. Trend lines are more direct than any indicator; as long as the price remains below MA120, all upward movements are merely rebounds rather than reversals.
In the past few days, Bitcoin has also been constantly hitting new lows, recalling the trend of the last cycle. The expectation for a rebound after breaking down with volume in place is 20%, back then from $26,700 to $32,399. Based on the current situation, it can only be inferred that only after breaking $70,000 will there be significant volume released, with rebound expectations reaching slightly above $80,000 at the weekly MA120, anticipating a 15% increase.
Additionally, if viewed from the cycle perspective, Bitcoin should bottom out in November to December. What could the price be then? $70,000? $60,000? Or even lower?
In the past few days, I reviewed the current bear market, which is basically following the same pattern as the last bear market.
Last cycle (May 2022): $30,000 was considered the iron bottom of the super cycle at that time because it was the starting point after the disaster on May 19, 2021, and also the support of the weekly 120. I particularly remember that when it broke below $30,000, the fear index was already below 10, and a lot of bottom-fishing funds believed it couldn't drop further or that it was time to rebound.
Current cycle (February 2026): $80,000, which used to be a strong support level, has now become a strong resistance level at the weekly MA120. $80,000 is also the cost price for Bitcoin mining companies. The current $76,000 feels like the brief struggle after breaking below $30,000 in the last cycle; the market may very well have one more action that thoroughly shatters confidence, such as touching the peak of the last cycle at $69,000.
If it falls below $70,000, it will trigger a larger-scale stop-loss and liquidation, which could potentially exceed the massive trading volume from November 2025. Without extreme panic, rebounds often just serve to entice buyers.
During the cycle, never let emotions lead you astray; in extreme trend markets, emotional indicators can be misleading. A fear index of 10 indicates that retail investors are already hopeless, but the main players may still be using this despair for one last deep squat. Trend lines are more direct than any indicator; as long as the price remains below MA120, all upward movements are merely rebounds rather than reversals.
In the past few days, Bitcoin has also been constantly hitting new lows, recalling the trend of the last cycle. The expectation for a rebound after breaking down with volume in place is 20%, back then from $26,700 to $32,399. Based on the current situation, it can only be inferred that only after breaking $70,000 will there be significant volume released, with rebound expectations reaching slightly above $80,000 at the weekly MA120, anticipating a 15% increase.
Additionally, if viewed from the cycle perspective, Bitcoin should bottom out in November to December. What could the price be then? $70,000? $60,000? Or even lower?
A lot of folks looking at @Pixels will first focus on the tokenomics, like distribution, staking, and liquidity. But if you zoom out a bit, you'll see it's more dependent on data.
The whole system runs on tracking user behavior across different games, then using that data to tweak reward distribution. The whitepaper mentions that it will continuously collect retention, payment, behavior paths, and other info, and keep optimizing the incentive strategies through models.
This will gradually create a network effect. The more games that are integrated, the richer the data dimensions; the more data there is, the easier it is for the models to identify which behaviors are more valuable; with improved distribution efficiency, it could attract more projects to onboard.
Structurally, this leans more towards a data-driven platform.
But whether this logic holds up has some prerequisites. Data needs to accumulate continuously while maintaining quality. If the number of games is limited, or if the behavioral data is too noisy, the model's effectiveness will be impacted.
Additionally, data itself is just the foundation; the key is whether it can be converted into more efficient growth. If it just piles up without significantly improving deployment effectiveness, the network effect will be hard to achieve.
So, understanding PIXEL from the perspective of a data platform makes sense, but in the end, we need to see if the data scale and actual results can keep amplifying. #pixel $PIXEL #广场征文
The whole system runs on tracking user behavior across different games, then using that data to tweak reward distribution. The whitepaper mentions that it will continuously collect retention, payment, behavior paths, and other info, and keep optimizing the incentive strategies through models.
This will gradually create a network effect. The more games that are integrated, the richer the data dimensions; the more data there is, the easier it is for the models to identify which behaviors are more valuable; with improved distribution efficiency, it could attract more projects to onboard.
Structurally, this leans more towards a data-driven platform.
But whether this logic holds up has some prerequisites. Data needs to accumulate continuously while maintaining quality. If the number of games is limited, or if the behavioral data is too noisy, the model's effectiveness will be impacted.
Additionally, data itself is just the foundation; the key is whether it can be converted into more efficient growth. If it just piles up without significantly improving deployment effectiveness, the network effect will be hard to achieve.
So, understanding PIXEL from the perspective of a data platform makes sense, but in the end, we need to see if the data scale and actual results can keep amplifying. #pixel $PIXEL #广场征文
A lot of projects talk about flywheels, and the logic seems pretty straightforward: distribute rewards to attract users, users engage and make purchases, and the revenue flows back into rewards, creating a continuous amplifying cycle.
The path of @Pixels follows a similar structure: staking generates a budget, rewards attract users, users make purchases, revenue flows back into the system, and then it enters the next distribution round.
The issue lies in the stability of each link in this chain. The consumption part is often the most overlooked.
If users only enter to grab rewards without any real payment activity, the chain gets stuck in the middle. The earlier steps can be quickly stacked through incentives, but once it comes to revenue backflow, without proper support, the following cycle struggles to continue.
Looking further down, even if consumption exists, it needs to have different sources. If it's just a flow of funds between players without any new value entering the system, the significance of this backflow gets weakened.
Additionally, the accuracy of reward distribution is crucial. If incentives are directed more towards short-term actions rather than long-term retention or paying users, initial metrics may look good, but a decline could still occur later.
Therefore, whether this flywheel can operate depends heavily on whether users are willing to keep paying and if the consumption has real support.
Until these conditions are validated, treating it as a structure that is still in operation will be closer to the actual situation. #pixel $PIXEL
The path of @Pixels follows a similar structure: staking generates a budget, rewards attract users, users make purchases, revenue flows back into the system, and then it enters the next distribution round.
The issue lies in the stability of each link in this chain. The consumption part is often the most overlooked.
If users only enter to grab rewards without any real payment activity, the chain gets stuck in the middle. The earlier steps can be quickly stacked through incentives, but once it comes to revenue backflow, without proper support, the following cycle struggles to continue.
Looking further down, even if consumption exists, it needs to have different sources. If it's just a flow of funds between players without any new value entering the system, the significance of this backflow gets weakened.
Additionally, the accuracy of reward distribution is crucial. If incentives are directed more towards short-term actions rather than long-term retention or paying users, initial metrics may look good, but a decline could still occur later.
Therefore, whether this flywheel can operate depends heavily on whether users are willing to keep paying and if the consumption has real support.
Until these conditions are validated, treating it as a structure that is still in operation will be closer to the actual situation. #pixel $PIXEL
Many projects mention DAO and decentralized decision-making in their design, but when it comes to the execution layer, power often doesn't lie in voting, but in how the rules are written.
In the mechanism of @Pixels , there are similar situations. On the surface, staking can influence resource allocation, and players can vote to support different games through staking. But if we dig deeper, we find that who receives the rewards is more determined by the data model.
The white paper repeatedly emphasizes one thing: reward distribution is data-driven and will continuously optimize through models based on user behavior, retention, payment, and other metrics.
This implies a reality: staking determines where the money flows, but the model determines who receives the money. In other words, the former is more like direction, while the latter is execution.
This creates a deviation. From a narrative perspective, it is user participation in distribution; from a mechanism perspective, it is algorithms filtering effective behaviors. This is not the problem itself, but rather a trade-off.
If we rely entirely on manual processes or voting, it is difficult to achieve refined distribution; introducing models can improve efficiency, but it also means the rules become more of a black box.
The key is whether this model is transparent, stable, and truly optimizing long-term value rather than short-term metrics.
Therefore, instead of simply classifying it as DAO or centralized, it is better to understand it as: the surface is decentralized participation, while the underlying is algorithm-driven distribution.
#pixel $PIXEL #广场征文
In the mechanism of @Pixels , there are similar situations. On the surface, staking can influence resource allocation, and players can vote to support different games through staking. But if we dig deeper, we find that who receives the rewards is more determined by the data model.
The white paper repeatedly emphasizes one thing: reward distribution is data-driven and will continuously optimize through models based on user behavior, retention, payment, and other metrics.
This implies a reality: staking determines where the money flows, but the model determines who receives the money. In other words, the former is more like direction, while the latter is execution.
This creates a deviation. From a narrative perspective, it is user participation in distribution; from a mechanism perspective, it is algorithms filtering effective behaviors. This is not the problem itself, but rather a trade-off.
If we rely entirely on manual processes or voting, it is difficult to achieve refined distribution; introducing models can improve efficiency, but it also means the rules become more of a black box.
The key is whether this model is transparent, stable, and truly optimizing long-term value rather than short-term metrics.
Therefore, instead of simply classifying it as DAO or centralized, it is better to understand it as: the surface is decentralized participation, while the underlying is algorithm-driven distribution.
#pixel $PIXEL #广场征文
I used to understand staking as very simple: locking assets, earning returns, and participating in network security.
But in the design of @Pixels , this logic has been changed in a direction.
It makes you bet on the game. The white paper states very directly: validators are no longer nodes, but the game itself
The change this brings is that staking is more like a resource allocation.
When you stake $PIXEL in a game, you are actually deciding one thing:
how much reward and how much user acquisition budget this game can get.
In other words, staking is more like voting for traffic. This is clearly different from traditional PoS.
In the past: the more you staked, the more block rewards you received;
now: the more you stake, the higher the weight of this game in the ecosystem.
Theoretically, this will create a competitive relationship.
Games need to improve retention and payment capabilities to attract more staking; while stakers flow between different games based on returns and performance. It sounds closer to a market mechanism, but the problem lies here.
The premise for this model to work is that the indicators must be credible.
If retention and revenue data cannot truly reflect quality, or are easily manipulated, then the resources allocated through staking may also be misguided.
Additionally, from the user's perspective, this type of staking is also closer to betting; you not only have to look at APR but also judge the future performance of this game.
So, Pixels is indeed trying to reconstruct the logic of staking, but currently, it feels more like a new allocation method rather than an already validated standard.
#pixel $PIXEL #广场征文
But in the design of @Pixels , this logic has been changed in a direction.
It makes you bet on the game. The white paper states very directly: validators are no longer nodes, but the game itself
The change this brings is that staking is more like a resource allocation.
When you stake $PIXEL in a game, you are actually deciding one thing:
how much reward and how much user acquisition budget this game can get.
In other words, staking is more like voting for traffic. This is clearly different from traditional PoS.
In the past: the more you staked, the more block rewards you received;
now: the more you stake, the higher the weight of this game in the ecosystem.
Theoretically, this will create a competitive relationship.
Games need to improve retention and payment capabilities to attract more staking; while stakers flow between different games based on returns and performance. It sounds closer to a market mechanism, but the problem lies here.
The premise for this model to work is that the indicators must be credible.
If retention and revenue data cannot truly reflect quality, or are easily manipulated, then the resources allocated through staking may also be misguided.
Additionally, from the user's perspective, this type of staking is also closer to betting; you not only have to look at APR but also judge the future performance of this game.
So, Pixels is indeed trying to reconstruct the logic of staking, but currently, it feels more like a new allocation method rather than an already validated standard.
#pixel $PIXEL #广场征文
When I first looked at vPIXEL, my initial reaction was quite simple: this is about liquidity stratification.
On the surface, it gives users another option — they can withdraw a token that can only be consumed or staked without any fees, or they can withdraw the main token but have to pay a fee. From an experience perspective, this indeed lowers the cost of staying within the ecosystem.
But if we look at it from a different angle, in terms of liquidity, this design resembles rearranging the selling path.
The past model was very straightforward: users received rewards → withdrew tokens → sold them.
The entire process was linear and had almost no resistance.
After introducing vPIXEL, this path has been split.
If users choose to withdraw without fees, they enter a 'can only use, cannot sell' state; if they insist on withdrawing the main token, they have to bear additional costs.
The result is that a portion of the selling pressure that would originally flow directly to the market is delayed or even kept within the system.
The question is whether this change optimizes the experience or controls the selling pressure.
From the user's perspective, it indeed provides a more flexible choice, especially for those who originally intended to continue participating; vPIXEL is more convenient. But from a system perspective, it essentially guides users to reduce direct monetization.
The white paper also mentions that it hopes to reduce selling pressure in this way and keep more value circulating within the ecosystem.
Thus, a more reasonable understanding is that two things coexist:
On one hand, it indeed improves the usage path for some users;
On the other hand, it also consciously implements stratified management of liquidity.
The key is whether there will be enough usage scenarios to accommodate the value that has been left behind. If not, the delayed liquidity will ultimately return to the market. #pixel $PIXEL #广场征文
On the surface, it gives users another option — they can withdraw a token that can only be consumed or staked without any fees, or they can withdraw the main token but have to pay a fee. From an experience perspective, this indeed lowers the cost of staying within the ecosystem.
But if we look at it from a different angle, in terms of liquidity, this design resembles rearranging the selling path.
The past model was very straightforward: users received rewards → withdrew tokens → sold them.
The entire process was linear and had almost no resistance.
After introducing vPIXEL, this path has been split.
If users choose to withdraw without fees, they enter a 'can only use, cannot sell' state; if they insist on withdrawing the main token, they have to bear additional costs.
The result is that a portion of the selling pressure that would originally flow directly to the market is delayed or even kept within the system.
The question is whether this change optimizes the experience or controls the selling pressure.
From the user's perspective, it indeed provides a more flexible choice, especially for those who originally intended to continue participating; vPIXEL is more convenient. But from a system perspective, it essentially guides users to reduce direct monetization.
The white paper also mentions that it hopes to reduce selling pressure in this way and keep more value circulating within the ecosystem.
Thus, a more reasonable understanding is that two things coexist:
On one hand, it indeed improves the usage path for some users;
On the other hand, it also consciously implements stratified management of liquidity.
The key is whether there will be enough usage scenarios to accommodate the value that has been left behind. If not, the delayed liquidity will ultimately return to the market. #pixel $PIXEL #广场征文
If we take the previously mentioned @Pixels and treat RORS as the core indicator, then between 0.8 and 1.0, it is actually a very critical boundary.
According to the data disclosed in the white paper, the current RORS is approximately around 0.8. This means that for every 1 unit of reward issued, only 0.8 can be brought back in return, which essentially still subsidizes the users. The model can operate, but it's not yet self-consistent.
Once this value approaches or even exceeds 1, the logic will change. Rewards will begin to have the meaning of input and output. Simply put, it shifts from spending money to buy growth, to making money from growth.
The problem is that this gap of 0.2 may seem small, but it could actually be the hardest part to cross. Because it not only involves optimizing distribution efficiency, but also whether users are genuinely paying, whether the game has sufficient appeal, and whether the data model can accurately filter valid behaviors.
In other words, the improvement of RORS is not a linear process, but more like a critical point issue. If it cannot be reached, the system still relies on external funding; once it is crossed, it may enter a positive cycle.
From a market perspective, this state itself presents a game space. Some people will price in advance based on approaching 1, believing that the model has a chance to run smoothly; while others will be more conservative, believing that until it truly exceeds 1, it still belongs to the unverified stage.
Therefore, this interval from 0.8 to 1.0 is essentially a process. What determines value is whether it is continuously approaching that critical point. #pixel $PIXEL #广场征文
According to the data disclosed in the white paper, the current RORS is approximately around 0.8. This means that for every 1 unit of reward issued, only 0.8 can be brought back in return, which essentially still subsidizes the users. The model can operate, but it's not yet self-consistent.
Once this value approaches or even exceeds 1, the logic will change. Rewards will begin to have the meaning of input and output. Simply put, it shifts from spending money to buy growth, to making money from growth.
The problem is that this gap of 0.2 may seem small, but it could actually be the hardest part to cross. Because it not only involves optimizing distribution efficiency, but also whether users are genuinely paying, whether the game has sufficient appeal, and whether the data model can accurately filter valid behaviors.
In other words, the improvement of RORS is not a linear process, but more like a critical point issue. If it cannot be reached, the system still relies on external funding; once it is crossed, it may enter a positive cycle.
From a market perspective, this state itself presents a game space. Some people will price in advance based on approaching 1, believing that the model has a chance to run smoothly; while others will be more conservative, believing that until it truly exceeds 1, it still belongs to the unverified stage.
Therefore, this interval from 0.8 to 1.0 is essentially a process. What determines value is whether it is continuously approaching that critical point. #pixel $PIXEL #广场征文
Next, please watch the hawthorn coin performance. Do you dare to believe what Boss Yi said?
Not long ago, he went long on ETH and lost 700 million USD.
Not long ago, he went long on ETH and lost 700 million USD.
If we look at RORS separately, it actually corresponds to a very traditional concept: return on ad spend.
In Web2, advertising spending is usually measured by ROAS, which means the money spent and how much revenue it ultimately brings back. This is a very basic metric, and almost all growth decisions revolve around it. The problem is that on-chain projects have rarely had similar measurement methods in the past, relying more on indirect indicators such as user numbers and transaction volumes.
The RORS proposed by @Pixels essentially does something similar: treating reward distribution as a form of spending behavior and then calculating whether this investment has brought actual returns. Currently, this value is about 0.8, and it has not yet reached break-even.
The significance of this change is that it provides a more direct judgment framework. Compared to simply looking at activity or growth speed, RORS is closer to "what results were achieved for the unit cost." If this metric can continue to improve, it at least indicates that reward distribution is becoming more effective.
However, comparing it to ROAS does not mean it has the same level of stability. Traditional advertising systems have mature data frameworks and long-term validation, while user behavior in the on-chain environment is more complex and has more interfering factors. Whether RORS can stably reflect the true situation needs to be verified.
Therefore, PIXEL is a metric that is currently being tried to establish. It indeed shifts the discussion from how fast growth is to whether growth has value, but whether it can ultimately become a universal framework still depends on its performance in actual operation.
#pixel $PIXEL #EssayContest
In Web2, advertising spending is usually measured by ROAS, which means the money spent and how much revenue it ultimately brings back. This is a very basic metric, and almost all growth decisions revolve around it. The problem is that on-chain projects have rarely had similar measurement methods in the past, relying more on indirect indicators such as user numbers and transaction volumes.
The RORS proposed by @Pixels essentially does something similar: treating reward distribution as a form of spending behavior and then calculating whether this investment has brought actual returns. Currently, this value is about 0.8, and it has not yet reached break-even.
The significance of this change is that it provides a more direct judgment framework. Compared to simply looking at activity or growth speed, RORS is closer to "what results were achieved for the unit cost." If this metric can continue to improve, it at least indicates that reward distribution is becoming more effective.
However, comparing it to ROAS does not mean it has the same level of stability. Traditional advertising systems have mature data frameworks and long-term validation, while user behavior in the on-chain environment is more complex and has more interfering factors. Whether RORS can stably reflect the true situation needs to be verified.
Therefore, PIXEL is a metric that is currently being tried to establish. It indeed shifts the discussion from how fast growth is to whether growth has value, but whether it can ultimately become a universal framework still depends on its performance in actual operation.
#pixel $PIXEL #EssayContest
Many counterfeit products have long ceased to have a stronghold, and even the main force is absent; if you rush in, you are the main force, and who can you expect to pull the market up?
Most P2E projects fail mainly because what is distributed does not translate into any sustainable results. Users take away profits, do not stay, and do not bring in new revenue. The result is that rewards become a one-way expense.
@Pixels in the white paper condenses this issue into a more specific metric: RORS (Return on Reward Spend). It can be simply understood as how much value can be brought back for every unit of reward distributed. Currently, this value is about 0.8, indicating that overall it has not yet reached a break-even point.
The significance of this metric is that it turns the concept of growth from a narrative into a verifiable result. In the past, when discussing whether a project is effective, more attention was paid to user numbers, activity levels, and community size. These metrics can be easily amplified by short-term incentives but do not necessarily represent true quality. RORS directly ties rewards to revenue, making the logic more straightforward.
Within this framework, rewards are more like an investment. Only when user behavior can bring subsequent value, such as payment or long-term retention, does this investment count as valid.
Of course, the introduction of the metric itself does not mean the problem is solved. RORS has not yet exceeded 1, meaning the model is still in the experimental stage. If it cannot continue to improve in the future, this system will still face pressures similar to traditional P2E.
Therefore, Pixels has provided a clearer measurement method. What we need to look at next is whether this value can truly enter a positive cycle.
#pixel $PIXEL #广场征文
@Pixels in the white paper condenses this issue into a more specific metric: RORS (Return on Reward Spend). It can be simply understood as how much value can be brought back for every unit of reward distributed. Currently, this value is about 0.8, indicating that overall it has not yet reached a break-even point.
The significance of this metric is that it turns the concept of growth from a narrative into a verifiable result. In the past, when discussing whether a project is effective, more attention was paid to user numbers, activity levels, and community size. These metrics can be easily amplified by short-term incentives but do not necessarily represent true quality. RORS directly ties rewards to revenue, making the logic more straightforward.
Within this framework, rewards are more like an investment. Only when user behavior can bring subsequent value, such as payment or long-term retention, does this investment count as valid.
Of course, the introduction of the metric itself does not mean the problem is solved. RORS has not yet exceeded 1, meaning the model is still in the experimental stage. If it cannot continue to improve in the future, this system will still face pressures similar to traditional P2E.
Therefore, Pixels has provided a clearer measurement method. What we need to look at next is whether this value can truly enter a positive cycle.
#pixel $PIXEL #广场征文
WLFI governance proposal launched, World Liberty Financial has put forward a governance proposal involving the restructuring of 6.228 billion WLFI tokens, with 4.5 billion tokens being burned. Additionally, there's good news for users: USD1 continues to be replenished, which is quite nice for financial management.
Founder/Team/Advisor Tokens (~45.24 billion): The vesting period has been changed to a 5-year linear release + 2-year cliff. If the new terms are accepted, 10% of the holdings (~4.52 billion) will be permanently destroyed immediately after the vote passes.
Early Supporter Tokens (~17.04 billion): The vesting period has been extended to 4 years, including 2 years of cliff, with no destruction.
This changes the previous indefinite lock-up state, which was uncertain when it would be released, to a clear release curve. The cost is that the team burns 10% of their share.
Voting Rules: A seven-day voting period, with a minimum threshold of 1 billion tokens.
The market has always been uncertain about the unlock timing for these tokens. This time it provides an explanation.
USD1 activity continues to be replenished.
New round of USD1 holdings airdrop on Binance: $15 million WLFI prize pool, from April 17 to May 15, distributed over 4 weeks. Spot, funding, margin, and contract accounts holding USD1 can participate, with a 1.2x bonus for margin and contract accounts. Just keep it in the contract account (no need to place trades).
This is already the fourth consecutive round this year. In January, $40 million, in February 235 million tokens, continued in March, and added another round in April. The current supply of USD1 is approximately $5.4 billion, ranking in the top 5 stablecoins, and the ongoing subsidies to capture market share show no signs of stopping. While the increase in stablecoin share is a good thing, it puts pressure on WLFI.
$USD1 $WLFI
Founder/Team/Advisor Tokens (~45.24 billion): The vesting period has been changed to a 5-year linear release + 2-year cliff. If the new terms are accepted, 10% of the holdings (~4.52 billion) will be permanently destroyed immediately after the vote passes.
Early Supporter Tokens (~17.04 billion): The vesting period has been extended to 4 years, including 2 years of cliff, with no destruction.
This changes the previous indefinite lock-up state, which was uncertain when it would be released, to a clear release curve. The cost is that the team burns 10% of their share.
Voting Rules: A seven-day voting period, with a minimum threshold of 1 billion tokens.
The market has always been uncertain about the unlock timing for these tokens. This time it provides an explanation.
USD1 activity continues to be replenished.
New round of USD1 holdings airdrop on Binance: $15 million WLFI prize pool, from April 17 to May 15, distributed over 4 weeks. Spot, funding, margin, and contract accounts holding USD1 can participate, with a 1.2x bonus for margin and contract accounts. Just keep it in the contract account (no need to place trades).
This is already the fourth consecutive round this year. In January, $40 million, in February 235 million tokens, continued in March, and added another round in April. The current supply of USD1 is approximately $5.4 billion, ranking in the top 5 stablecoins, and the ongoing subsidies to capture market share show no signs of stopping. While the increase in stablecoin share is a good thing, it puts pressure on WLFI.
$USD1 $WLFI
The SEC abolishes the threshold for 'day trading', bringing the biggest rule change for retail investors in 25 years
On April 14, the SEC officially approved the FINRA proposal, abolishing the Pattern Day Trading (PDT) rule that has been in place for 25 years.
Three core changes
1. Eliminating the $25,000 minimum account threshold
2. Removing the 'day trader' identity label
3. Replacing the original trading frequency limits with a real-time risk margin system
Translated into simple terms: if you don’t have money, don’t engage in day trading. This rule has existed for 25 years. In 25 years, the market structure, trading technology, and participant composition have all changed, yet the rule hasn’t budged an inch.
Who are the direct beneficiaries?
1. Commission-free brokers
Increased trading frequency directly boosts revenue. HOOD (Robinhood) is the most affected—its user base has the highest proportion of small accounts, which were most restricted by PDT, and now this batch of users has all been released. IBKR and SCHW are similar.
2. High-volatility assets
More day trading capital flowing in = amplified volatility. AI concept stocks, small-cap tech stocks, and actively traded options will become the main contributors to the new trading volume.
3. Trading tools and data service providers
An increase in day traders → Increased demand for real-time data, technical analysis tools, and trading signals.
The essence of this rule is to hand a problem back to the market from regulators: Are you capable of surviving in a freer environment?
Information source: SEC document SR-FINRA-2025-017, approved on April 14, 2026. If you were able to catch this information early, then the recent rise of HOOD is quite considerable.
$HOOD
On April 14, the SEC officially approved the FINRA proposal, abolishing the Pattern Day Trading (PDT) rule that has been in place for 25 years.
Three core changes
1. Eliminating the $25,000 minimum account threshold
2. Removing the 'day trader' identity label
3. Replacing the original trading frequency limits with a real-time risk margin system
Translated into simple terms: if you don’t have money, don’t engage in day trading. This rule has existed for 25 years. In 25 years, the market structure, trading technology, and participant composition have all changed, yet the rule hasn’t budged an inch.
Who are the direct beneficiaries?
1. Commission-free brokers
Increased trading frequency directly boosts revenue. HOOD (Robinhood) is the most affected—its user base has the highest proportion of small accounts, which were most restricted by PDT, and now this batch of users has all been released. IBKR and SCHW are similar.
2. High-volatility assets
More day trading capital flowing in = amplified volatility. AI concept stocks, small-cap tech stocks, and actively traded options will become the main contributors to the new trading volume.
3. Trading tools and data service providers
An increase in day traders → Increased demand for real-time data, technical analysis tools, and trading signals.
The essence of this rule is to hand a problem back to the market from regulators: Are you capable of surviving in a freer environment?
Information source: SEC document SR-FINRA-2025-017, approved on April 14, 2026. If you were able to catch this information early, then the recent rise of HOOD is quite considerable.
$HOOD
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